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This study aims to investigate whether objective and subjective rationality affects individual voters’ use of accounting information and if such use affects voting behavior. While prior accounting studies assume voter rationality concerning financial performance and political outcomes, this study distinguishes between two types of voters: objective rational voters (who make voting decisions about multiple alternatives based on objective information) and subjective rational voters (who make decisions based on their subjective values, and thus do not explore information or explore only information biased toward one alternative). This study expects that accounting information can influence the voting behavior of objective and subjective rational voters.

Focusing on the 2020 Osaka Metropolitan Plan Referendum, this study used an online survey conducted on 768 respondents after the referendum.

This study finds that objective rational voters use accounting information more than subjective rational voters, voters who used accounting information were more likely to vote against the referendum, and voting behavior is not directly affected by the type of rationality of voters; rather, objective rational voters are more likely to use accounting information that has a mediating effect on voting behavior.

The results advance the understanding of public sector accounting research and practices by providing evidence of the individual voter’s use of accounting information and their voting behavior in political contexts.

The purpose of this study was to utilise a lived experience (LE) informed/co-designed approach to explore the service-user experience of using the reasons for use package (RFUP) within a youth residential rehabilitation mental health setting.

LE researchers (those who have lived through mental illness or distress), Master of social work students, a community of mental health service manager, community of mental health researchers, dual diagnosis service researchers and university-based researchers collaborated on the project. The study used an exploratory, qualitative approach of semi-structured interviews to invite young people's experiences of the resource. The research team conducted a collaborative thematic analysis drawing on the range of perspectives.

Through five interviews with young people, key themes identified included: client factors and extra-therapeutic events, relationship factors, technique/model factors/delivery and outcomes/things noticed.

The RFUP was a useful clinical tool with the young people in this pilot as it improved awareness of reasons for drug use and impact on mental health, service user to staff relationship, quality of the resource, mode of delivery and participant self-knowledge.

Young people valued the supportive role that the RFUP played in facilitating positive relationships with their workers.

A brief review of earlier studies on the productivity scenario of Indian industry shows that most of the studies analysed are confined to either parametric approach or growth accounting approach of measuring productivity. At the same time, the few studies based on the non-parametric [namely, Malmquist productivity index (MPI)] overlook the returns to scale conditions as well as the bias involved in the estimation of distance functions. Given this backdrop, this study aims to provide a robust measure of productivity, which considers the returns to scale assumptions and correct for the bias involved in the estimation of productivity.

This study empirically tests for the returns to scale that exists in the chemical and chemical products industry in India. The test result suggests that Ray and Desli (1997) approach of MPI is the appropriate one for the present context. Initially, the conventional Ray and Desli (1997) estimation and decomposition of MPI for the period 2001 to 2017 is being used. Subsequently, to correct for the bias in the estimation of efficiency scores used for the estimation of MPI, the bootstrapping algorithm of Simar and Wilson (2007) has been extended into the context of MPI estimation.

The results from the conventional Malmquist productivity estimates testifies to an improvement of total factor productivity (TFP) in seven out of 16 years under consideration. On the contrary, TFP growth is recorded only in the four years throughout the period after the bias correction. A greater discrepancy between the two measures has been found in the case of scale change factor component of MPI.

The technical change (TC) component positively influences TFP, whereas scale change factor (SCF) deteriorates the TFP condition of this industry. It will be appropriate for these firms to identify and operate under an optimal scale of operation, along with reaping the benefits of technological change. From a methodological perspective, researchers should consider the potential bias that arise in estimation of TFP and use a larger sample whenever possible.

This paper brings in a new perspective to the existing literature on industrial productivity. As against earlier studies, this study empirically tests the returns to scale of the sector under consideration and uses the most appropriate approach to measure productivity. The effect of sampling bias on TFP and its components is analysed.

This article aims to gain a better understanding of the reasons why serendipity is designed for in different kinds of environments. Understanding these design intents sheds light on the value such designs bring to designers, in contrast to the users of the environment. In this way, the article seeks to contribute to the literature on cultivating serendipity from a designers’ point of view.

An extensive review of the literature discussing designing for serendipity was conducted to elicit the different motivations to design for serendipity. Based on these findings and a thorough analysis, a typology of design intents for serendipity is presented.

The article puts forward four intents to design for serendipity: serendipity as an ideal, common good, mediator and feature. It also highlights that the current academic discourse puts a strong emphasis on two of them. It is argued that this academic abstraction could be problematic for how we deal with designs for serendipity, both in theory and practice.

The novelty of this article is that it addresses the question of why to design for serendipity from a designer’s point of view. By introducing the notion of directionality it opens up the opportunity to discuss serendipity from multiple perspectives, which contributes to gaining a firmer understanding of serendipity. It allows to more explicitly formulate the different functions of a design for serendipity and thereby expands our knowledge on the value of designing for serendipity. At the same time, it sheds light on the potential threats to designing for serendipity.

Fear grips stock markets when a pandemic like COVID-19 strikes, severely affecting stock prices. However, fundamental value drivers of companies do not change drastically during pandemics. The sensitivity of firms' cash flows to lockdowns during pandemics depends on their cost structure. This paper develops a financial model incorporating information about value drivers and lockdown sensitivity of companies to find the enterprise value.

The authors develop a financial model that estimates the effects of COVID-19 on enterprise value and helps to identify wrongly valued stocks. The authors apply the model to five Indian stocks from five different industries to study how firms belonging to various sectors get affected differently in this pandemic.

Companies belonging to civil aviation and retail sectors get more affected by COVID-19 compared to those in movie exhibition, automobile and hotel industries. The cost structure of the latter category of firms reduces their cash flow effect.

The model can be used by practitioners to understand any pandemic's effect on stock prices. Also, it explains how firms having different cost structures get affected by any crisis and help investors in taking appropriate buy/sell decisions.

The study has two contributions: first, the authors develop a financial model to estimate the effect of COVID-19 on the enterprise value. Second, contrary to popular perception, the authors find companies belonging to movie exhibition, hotel and automobile industries do not get that severely affected.

This article analyzes the hypothesis that analysts issue higher long-term earnings growth (LTG) forecasts following a market-wide investor sentiment.

This study analyzed 193 publicly traded Brazilian firms listed on B3 (Brasil, Bolsa, Balcão), totaling 2,291 observations. To address the potential selection bias resulting from analysts' preference for more liquid firms, this study used the Heckman model in the analysis with samples with only one analyst and the entire sample. The study also applied other robustness tests to ensure the reliability of the findings.

The results suggest that market-wide investor sentiment influences LTG when the firm's stocks are difficult to value. Market optimism did not reflect five-year profit growth after the forecast issue, suggesting lower forecast accuracy during high investor sentiment values.

Volatile-earnings firms have relevant implications in LTG forecasts during bullish moments. According to the study’s evidence, investors' decisions and policymakers' and regulators' rules should consider analysts' expertise as independent information when considering LTG as input for valuation models, even under market optimism.

This paper contributes to the literature on the influence of investor sentiment on analysts' forecasts by incorporating two crucial elements in the discussion: the scenario free from herding behavior, as usually only one analyst issues LGT forecast for Brazilian firms, and the analysis of research hypotheses incorporates the difficulty of pricing a firm given the uncertainty of its earnings as an explanation to bullish forecast.

The aim of this work is to explain the final negotiated prices for some of the most famous transfers of football players over the last twelve years (2007–2018).

The article analyses different values for forwards taken from the sports website Transfermarkt, developing a statistical model based on personal, performance, risk, environmental and popularity variables. From those values, the article finds an explanation for the final prices paid for 20 superstar players based on a combination of real option valuations, incorporating the players' life cycles and game theory.

The authors find that in a large percentage (70%) of the analysed cases, the price paid was higher than the intrinsic market value resulting from Transfermarkt, implying the existence of monopolistic rents, paid as “growth options” on prices from different negotiating conditions. On occasions, the final prices also exceed the value of the growth option, calculated under neutral bargaining conditions, highlighting the lack of economic viability of important transfers, leading to financial difficulties for the clubs involved.

The algorithm provides more flexibility and realism than previous proposals, based on the life cycle of football players, introducing the uncertainty and volatility of projections through Monte Carlo simulation, the capacity of clubs to bargain a price at any point of the contract and finally, the buyer's ability to transfer the player if his subsequent performance is not as expected.